Causal inference is the process of determining whether and how one variable influences another, going beyond simple correlations and attempting to uncover cause-and-effect relationships. It plays a crucial role in fields like medicine, economics, and social sciences, where understanding the impact of interventions or policies is essential. Unlike traditional statistical analysis, causal inference requires careful consideration of study design, confounding factors, and the use of specialized methods such as randomized controlled trials, instrumental variables, and propensity score matching to draw valid conclusions about causality.
Recent Highlights
Distinguishing Cause from Effect with Causal Velocity Models
B. Bloem-Reddy
Causal inference is distinguished from standard statistical inference by the requirement to identify patterns arising from cause and effect, as opposed to purely correlation-driven patterns, which can be misleading for predicting the outcomes of interventions. Professor Bloem-Reddy and collaborators Xi, Dance, and Orbanz have formulated a new framework for causal modelling that is based on the mathematics of dynamical systems and measure transport, and that allows the use of cutting-edge machine learning methods on causal inference problems.